In this paper we demo the Temporal Game, a novel approach to temporal relation extraction that casts the task as an interactive game. Instead of directly annotating interval-level relations, our approach decomposes them into point-wise comparisons between the start and end points of temporal entities. At each step, players classify a single point relation, and the system applies temporal closure to infer additional relations and enforce consistency. This point-based strategy naturally supports both interval and instant entities, enabling more fine-grained and flexible annotation than any previous approach. The Temporal Game also lays the groundwork for training reinforcement learning agents, by treating temporal annotation as a sequential decision-making task. To showcase this potential, the demo presented in this paper includes a Game mode, in which users annotate texts from the TempEval-3 dataset and receive feedback based on a scoring system, and an Annotation mode, that allows custom documents to be annotated and resulting timeline to be exported. Therefore, this demo serves both as a research tool and an annotation interface. The demo is publicly available at https://temporal-game.inesctec.pt, and the source code is open-sourced to foster further research and community-driven development in temporal reasoning and annotation.

There are a lot of popular games I haven't personally connected with, including The Sims, Minecraft, pretty much anything that looks like anime. Roblox is among them--oh yeah, Among Us too--and I've seen some of the more problematic issues with it. But last week, Roblox recorded 45 million people playing its various modes at once. According to Roblox CEO David Baszucki, the game-slash-service surpassed 45 million players on August 23rd. And I don't just mean for a single Steam game.

Collectible card games are challenging, widely played games that have received increasing attention from the AI research community in recent years. Despite important breakthroughs, the field still poses many unresolved challenges. This work aims to help further research on the genre by proposing a taxonomy of collectible card games by analyzing their rules, mechanics, and game modes from the perspective of game-playing AI research. To achieve this, we studied a set of popular games and provided a thorough discussion about their characteristics.

Though robustness of networks to random attacks has been widely studied, intentional destruction by an intelligent agent is not tractable with previous methods. Here we devise a single-player game on a lattice that mimics the logic of an attacker attempting to destroy a network. The objective of the game is to disable all nodes in the fewest number of steps. We develop a reinforcement learning approach using deep Q-learning that is capable of learning to play this game successfully, and in so doing, to optimally attack a network. Because the learning algorithm is universal, we train agents on different definitions of robustness and compare the learned strategies. We find that superficially similar definitions of robustness induce different strategies in the trained agent, implying that optimally attacking or defending a network is sensitive the particular objective. Our method provides a new approach to understand network robustness, with potential applications to other discrete processes in disordered systems.

Season five of Overwatch 2 will arrive on Tuesday, bringing with it updates such as a mythic skin for Tracer, a fantasy theme and a fresh limited-time game mode. However, it feels like a bit of a placeholder as Blizzard is already looking forward to the sixth season, which starts on August 10th. The studio is calling this the biggest update to Overwatch 2 yet. It'll be called Overwatch 2: Invasion and it will include co-op story missions . Long-promised hero missions, which were going to feature long-term progression features for all heroes, are no longer happening.

The ability to learn continually is essential in a complex and changing world. In this paper, we characterize the behavior of canonical value-based deep reinforcement learning (RL) approaches under varying degrees of non-stationarity. In particular, we demonstrate that deep RL agents lose their ability to learn good policies when they cycle through a sequence of Atari 2600 games. This phenomenon is alluded to in prior work under various guises -- e.g., loss of plasticity, implicit under-parameterization, primacy bias, and capacity loss. We investigate this phenomenon closely at scale and analyze how the weights, gradients, and activations change over time in several experiments with varying dimensions (e.g., similarity between games, number of games, number of frames per game), with some experiments spanning 50 days and 2 billion environment interactions. Our analysis shows that the activation footprint of the network becomes sparser, contributing to the diminishing gradients. We investigate a remarkably simple mitigation strategy -- Concatenated ReLUs (CReLUs) activation function -- and demonstrate its effectiveness in facilitating continual learning in a changing environment.

Editor's note: Ryan Smith is a graduate of East Carolina University and has been an avid gamer since the early and mid 90's. Other favorites include BioWare and Bethesda RPGs, Dark souls, Total War titles and various MMORPG's and Shooters. RALEIGH – Amid a sea of controversies over the last few years, Blizzard Entertainment (a subsidiary of Activision Blizzard) has also seen a massive player count drop in their pillar title World of Warcraft. Their RTS(real time strategy) titles, formerly among their best and most popular, are either relics of their former selves in the case of StarCraft 2, or abysmally received upon launch such as the case with Warcraft 3: Reforged, now infamously one of the worst reviewed games of all time. However, they did enter the world of First Person Shooters for the first time in 2016 with a massively popular title known as Overwatch.

You turn left, then right, weaving between pillars to collect precious orbs while dodging its advance. For a moment, it seems you've lost it. But then your foe appears around a corner, and bam! The familiar Game Over screen appears--but then, just below, something different. You lasted just a few seconds in this hair-raising chase, so, adrenaline still pumping, you tap "Hard."

Hisense, provider of high-performance televisions and appliances, announces the availability of its first ULED Google TV, the U6H. Serving as the brand's introduction to the premium TV category, the U6H offers premium features like Quantum Dot Color, Dolby Vision IQ, and Game Mode Plus, at an affordable price. The U6H will be available in four TV sizes ranging from 50 to 75-inches. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) "We've worked hard over the past few years to put premium TV options within people's reach, and we're continuing that in 2022 with our new lineup, specifically with our U6H" "We've worked hard over the past few years to put premium TV options within people's reach, and we're continuing that in 2022 with our new lineup, specifically with our U6H," said David Gold, President of Hisense USA. "With an even more robust audio and visual experience at a lower price point than our competitors, the 2022 U6 series is a Hisense television that's great for everyone."

In a broadcast on Thursday, the team behind "Overwatch 2" revealed the game will operate around nine-week seasons. Starting on Oct. 4, the sequel will have three new heroes, six new maps and the new game mode, Push. At the start of every season, Blizzard will release another hero, map or game mode as a free update to the game. The three heroes coming in October will include Sojourn, who was first announced this spring, Junker Queen, an Australian hero Blizzard unveiled on Sunday, and an unnamed support hero.